TREND: Criminal Justice Information Services Division, Federal Bureau of Investigation, Federal Bureau of Investigation. Offenses Known to Law Enforcement: Confirmed Crime Rate | State: Michigan | County: Alcona, Alger, Allegan, Alpena, Antrim, Arenac, Baraga, Barry, Bay, Benzie, Berrien, Branch, Calhoun, Cass, Charlevoix, Cheboygan, Chippewa, Clare, Clinton, Crawford, Delta, Dickinson, Eaton, Emmet, Genesee, Gladwin, Gogebic, Grand Traverse, Gratiot, Hillsdale, Houghton, Huron, Ingham, Ionia, Iosco, Iron, Isabella, Jackson, Kalamazoo, Kalkaska, Kent, Keweenaw, Lake, Lapeer, Leelanau, Lenawee, Livingston, Luce, Mackinac, Macomb, Manistee, Marquette, Mason, Mecosta, Menominee, Midland, Missaukee, Monroe, Montcalm, Montmorency, Muskegon, Newaygo, Oakland, Oceana, Ogemaw, Ontonagon, Osceola, Oscoda, Otsego, Ottawa, Presque Isle, Roscommon, Saginaw, Saint Clair, Saint Joseph, Sanilac, Schoolcraft, Shiawassee, Tuscola, Van Buren, Washtenaw, Wayne, Wexford | Crime Type*: All Crimes Reported, 1980 - 2017. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 010-001-004
Bibliographic record
Abstract
Criminal Justice Information Services Division, Federal Bureau of Investigation, Federal Bureau of Investigation. Offenses Known to Law Enforcement: Confirmed Crime Rate | State: Michigan | County: Alcona, Alger, Allegan, Alpena, Antrim, Arenac, Baraga, Barry, Bay, Benzie, Berrien, Branch, Calhoun, Cass, Charlevoix, Cheboygan, Chippewa, Clare, Clinton, Crawford, Delta, Dickinson, Eaton, Emmet, Genesee, Gladwin, Gogebic, Grand Traverse, Gratiot, Hillsdale, Houghton, Huron, Ingham, Ionia, Iosco, Iron, Isabella, Jackson, Kalamazoo, Kalkaska, Kent, Keweenaw, Lake, Lapeer, Leelanau, Lenawee, Livingston, Luce, Mackinac, Macomb, Manistee, Marquette, Mason, Mecosta, Menominee, Midland, Missaukee, Monroe, Montcalm, Montmorency, Muskegon, Newaygo, Oakland, Oceana, Ogemaw, Ontonagon, Osceola, Oscoda, Otsego, Ottawa, Presque Isle, Roscommon, Saginaw, Saint Clair, Saint Joseph, Sanilac, Schoolcraft, Shiawassee, Tuscola, Van Buren, Washtenaw, Wayne, Wexford | Crime Type*: All Crimes Reported, 1980 - 2017. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 010-001-004 Dataset: Reports crimes per 100,000 persons for crimes reported to law enforcement, by offense, state, county, and law enforcement agency. Statistics are presented for violent and property crimes. Shows crimes reported to police, by offense, state, county, and jurisdiction, and crime rate per 100,000 population. Data are from the Federal Bureau of Investigation (FBI) Uniform Crime Reporting (UCR) Program, which is a nationwide, cooperative statistical effort of more than 18,000 city, university and college, county, state, tribal, and federal law enforcement agencies voluntarily reporting data on crimes brought to their attention. The UCR Program collects offenses known to law enforcement for violent and property crime types (Part I offenses). Each month, participating law enforcement agencies submit information on the number of Part I offenses known to them (crimes reported). Violent crime is composed of four offenses: murder and non-negligent manslaughter, rape, robbery, and aggravated assault. Violent crimes are defined in the UCR Program as those offenses which involve force or threat of force. Property crime includes the offenses of burglary, larceny-theft, motor vehicle theft, and arson. The object of the theft-type offenses is the taking of money or property, but there is no force or threat of force against the victims. https://ucr.fbi.gov/word Category: Criminal Justice and Law Enforcement Subject: Assaults, Criminal Offenses, Homicides, Violence, Burglaries, Crime, Crime Rates, Felonies, Sexual Assaults, Homicide Rates, Property Crime, Larceny Source: Federal Bureau of Investigation The Federal Bureau of Investigations (FBI) is the principal investigative arm of the U.S. Department of Justice. The Uniform Crime Reporting (UCR) Program was conceived in 1929 by the International Association of Chiefs of Police to meet a need for reliable, uniform crime statistics for the nation. In 1930, the FBI was tasked with collecting, publishing, and archiving those statistics. A 5-year redesign effort to provide more comprehensive and detailed crime statistics resulted in the National Incident-Based Reporting System (NIBRS) which collects data on each reported crime incident. The UCR Program is currently being expanded to NIBRS. http://www.fbi.gov/
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.157 | 0.143 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".